Kinetics of Ferromagnetic Ordering in 3D Ising Model: Do we understand the case of zero temperature quench?

We study phase ordering dynamics in the three-dimensional nearest-neighbor Ising model, following rapid quenches from infinite to zero temperature. Results on various aspects, viz., domain growth, persistence, aging and pattern, have been obtained via the Glauber Monte Carlo simulations of the model on simple cubic lattice. These are analyzed via state-of-the-art methods, including the finite-size scaling, and compared with those for quenches to a temperature above the roughening transition. Each of these properties exhibit remarkably different behavior at the above mentioned final temperatures. Such a temperature dependence is absent in the two-dimensional case for which there is no roughening transition

Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.Comment: CVPR Workshops Proceedings 202

Coarsening in 3D Nonconserved Ising Model at Zero Temperature: Anomalies in structure and relaxation of order-parameter autocorrelation

Via Monte Carlo simulations we study pattern and aging during coarsening in nonconserved nearest neighbor Ising model, following quenches from infinite to zero temperature, in space dimension $d=3$. The decay of the order-parameter autocorrelation function is observed to obey a power-law behavior in the long time limit. However, the exponent of the power-law, estimated accurately via a state-of-art method, violates a well-known lower bound. This surprising fact has been discussed in connection with a quantitative picture of the structural anomaly that the 3D Ising model exhibits during coarsening at zero temperature. These results are compared with those for quenches to a temperature above that of the roughening transition.Comment: 15 pages, 6 figure

FUNDAMENTAL STUDIES OF SURFACTANT TEMPLATED METAL OXIDE MATERIALS SYNTHESIS AND TRANSFORMATION FOR ADSORPTION AND ENERGY APPLICATIONS

This work addresses fundamental aspects of designing templates and curing conditions for the synthesis of mesoporous metal oxide thin films. The first section addresses selection of cationic-carbohydrate surfactant mixtures to synthesize templated silica thin films for selective adsorption of simple carbohydrates based on molecular imprinting. Nuclear magnetic resonance and fluorescence spectroscopy results suggest a novel structure for mixtures of alkyl glucopyranosides or xylopyranosides with cationic (trimethylammonium) surfactants. Despite thermodynamically favorable mixing, the carbohydrate headgroups in the mixed micelle adopt an inverted configuration with their headgroups in the micelle core, and therefore are inaccessible for molecular imprinting. This orientation occurs even when the alkyl tail length of the carbohydrate surfactant is greater than that of the cationic surfactant, but this limitation can be overcome by introducing a triazole linker to the carbohydrate surfactant. The next section addresses the effects of aging conditions on the structural and chemical evolution of surfactant templated silica thin films. The third section describes the synthesis of carbohydrate/cationic surfactant imprinted silica thin films with orthogonally oriented cylindrical pores by modifying the glass surface with a random copolymer. The last part of the dissertation addresses the effect of pore orientation on the transformation mechanism of block copolymer templated titania thin films during high temperature curing. Mesoporous titania thin films can be used for photochemical and solar cell applications, but doing so requires addressing the tradeoff between loss of mesostructural order and growth of crystallinity during thermal treatment. By using advanced x-ray scattering techniques it has been shown that the titania films with vertically oriented pores can better withstand the anisotropic stress that develops during thermal treatment compare to titania films with mixed pore orientation. For instance, films with parallel or mixed pores can only be heated at 400 °C for a brief time (~10 min) without loss of order, while orthogonally oriented films can be heated at 550 °C or greater for extended time periods (on the order of hours) without significant loss of long-range mesopore structure. Detailed kinetic modeling was applied to enable the comparison of activation energy for mesostructure loss in films as a function of pore orientation and thickness

Detection and Explanation of Distributed Denial of Service (DDoS) Attack Through Interpretable Machine Learning

Distributed denial of service (DDoS) is a network-based attack where the aim of the attacker is to overwhelm the victim server. The attacker floods the server by sending enormous amount of network packets in a distributed manner beyond the servers capacity and thus causing the disruption of its normal service. In this dissertation, we focus to build intelligent detectors that can learn by themselves with less human interactions and detect DDoS attacks accurately. Machine learning (ML) has promising outcomes throughout the technologies including cybersecurity and provides us with intelligence when applied on Intrusion Detection Systems (IDSs). In addition, from the state-of-the-art ML-based IDSs, the Ensemble classifier (combination of classifiers) outperforms single classifier. Therefore, we have implemented both supervised and unsupervised ensemble frameworks to build IDSs for better DDoS detection accuracy with lower false alarms compared to the existing ones. Our experimentation, done with the most popular and benchmark datasets such as NSL-KDD, UNSW-NB15, and CICIDS2017, have achieved at most detection accuracy of 99.1% with the lowest false positive rate of 0.01%. As feature selection is one of the mandatory preprocessing phases in ML classification, we have designed several feature selection techniques for better performances in terms of DDoS detection accuracy, false positive alarms, and training times. Initially, we have implemented an ensemble framework for feature selection (FS) methods which combines almost all well-known FS methods and yields better outcomes compared to any single FS method.The goal of my dissertation is not only to detect DDoS attacks precisely but also to demonstrate explanations for these detections. Interpretable machine learning (IML) technique is used to explain a detected DDoS attack with the help of the effectiveness of the corresponding features. We also have implemented a novel feature selection approach based on IML which helps to find optimum features that are used further to retrain our models. The retrained model gives better performances than general feature selection process. Moreover, we have developed an explainer model using IML that identifies detected DDoS attacks with proper explanations based on effectiveness of the features. The contribution of this dissertation is five-folded with the ultimate goal of detecting the most frequent DDoS attacks in cyber security. In order to detect DDoS attacks, we first used ensemble machine learning classification with both supervised and unsupervised classifiers. For better performance, we then implemented and applied two feature selection approaches, such as ensemble feature selection framework and IML based feature selection approach, both individually and in a combination with supervised ensemble framework. Furthermore, we exclusively added explanations for the detected DDoS attacks with the help of explainer models that are built using LIME and SHAP IML methods. To build trustworthy explainer models, a detailed survey has been conducted on interpretable machine learning methods and on their associated tools. We applied the designed framework in various domains, like smart grid and NLP-based IDS to verify its efficacy and ability of performing as a generic model